Search

How to reduce the health risks for commuters, caused by air pollution such as PM2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on‐road PM2.5 throughout the whole city. First, the micro‐scale PM2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index‐based built‐up index, are incorporated within the TOA‐LUR modeling. On‐road PM2.5 distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM2.5. The proposed framework was applied in high‐density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models is at a high‐level with an R2 of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM2.5 distribution maps at high‐spatial‐resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high‐density cities. Plain Language Summary: Every year, exposure to outdoor air pollution has caused 7 million premature deaths and resulted in the loss of millions more healthy years of life. Therefore, it's critical to establish planning tools to support people's healthier and cleaner daily travel. By land use regression (LUR) modeling enhanced by the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors, the original framework proposed in this study provides an estimation and a mapping of on‐road PM2.5 at the fine scale, providing people with healthier travel choices. The estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models achieved a high R2 of 0.70–0.90. It may become a reference and guide for high‐density cities, for smart travel planning and refined monitoring of air pollution in urban areas. It is hoped, therefore, that this study assists more people in urban areas to reduce their exposure to outdoor air pollution, both to protect their health, and assist in the construction of healthy cities, especially for the high‐density city. Key Points: A novel framework was proposed for healthy route planning with less PM2.5 exposureEstimation models of fine scale on‐road PM2.5 have been proposed and validatedA mobile application that provides healthy travel route planning has been developed [ABSTRACT FROM AUTHOR]

Object-based convolutional neural networks (OCNNs) have achieved great performance in the field of land-cover and land-use classification. Studies have suggested that the generation of object convolutional positions (OCPs) largely determines the performance of OCNNs. Optimized distribution of OCPs facilitates the identification of segmented objects with irregular shapes. In this study, we propose a morphology-based binary tree sampling (BTS) method that provides a reasonable, effective, and robust strategy to generate evenly distributed OCPs. The proposed BTS algorithm consists of three major steps: 1) calculating the required number of OCPs for each object, 2) dividing a vector object into smaller sub-objects, and 3) generating OCPs based on the sub-objects. Taking the object identification in land-cover and land-use classification as a case study, we compare the proposed BTS algorithm with other competing methods. The results suggest that the BTS algorithm outperforms all other competing methods, as it yields more evenly distributed OCPs that contribute to better representation of objects, thus leading to higher object identification accuracy. Further experiments suggest that the efficiency of BTS can be improved when multi-thread technology is implemented. [ABSTRACT FROM AUTHOR]

Due to the increased outdoor transmission risk of new SARS-COV-2 variants, the health of urban residents in daily travel is being threatened. In the new normal of long-term coexistence with SARS-CoV-2, how to avoid being infected by SARS-CoV-2 in daily travel has become a key issue. Hence, a spatiotemporal solution has been proposed to assist healthy travel route planning. Firstly, an enhanced urban-community-scale geographic model was proposed to predict daily COVID-19 symptom onset risk by incorporating the real-time effective reproduction numbers, and daily population variation of fully vaccinated. On-road onset risk predictions in the next following days were then extracted for searching healthy routes with the least onset risk values. The healthy route planning was further implemented in a mobile application. Hong Kong, one of the representative highly populated cities, has been chosen as an example to apply the spatiotemporal solution. The application results in the four epidemic waves of Hong Kong show that based on the high accurate prediction of COVID-19 symptom onset risk, the healthy route planning could reduce people's exposure to the COVID-19 symptoms onset risk. To sum, the proposed solution can be applied to support the healthy travel of residents in more cities in the new normalcy., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)